Robust saliency detection via corner information and an energy function. Issue 6 (27th July 2017)

• Record Type: Journal Article
• Authors: Zhang, Hanling; Xia, Chenxing; Gao, Xiuju
• Is Part Of: IET computer vision. Volume 11:Issue 6(2017)
• Page Start: 379
• Page End: 388
• Publication Date: 2017-07-27
• Subjects: object detection -- genetic algorithms -- image matching; robust saliency detection -- corner information -- energy function -- visual saliency detection algorithm -- genetic algorithm -- manifold ranking -- image elements -- foreground seeds; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0492
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : In this study, the authors propose a distinctive bottom‐up visual saliency detection algorithm based on a new background prior and a new reinforcement. Inspired by genetic algorithm, the final map is obtained with three steps. First of all, the authors construct a background‐based saliency map by manifold ranking via superior image corners selected by convex‐hull as background prior, which is different from most of the existing background prior‐based methods treated all image boundaries as background. Then, a better result is obtained by ranking the relevance of the image elements with foreground seeds extracted from the preliminary saliency map. Furthermore, a novel optimisation framework is introduced with the intention of refining the map, which integrates an energy function with a guided filter. Experimental results on three public datasets indicate that the proposed method performs favourably against the state‐of‐the‐art algorithms.